The biggest surprise in 2017 was seeing all the dead elephants. Many of our clients have reached the consensus that Hadoop will no longer be the vehicle for big data success. Organizations are opting for a distributed file system and moving towards access technologies that are flexible, real-time, and serverless.

People are still shocked about how many polls got the sentiments of Americans wrong before the 2016 election. It was a classic case of "garbage in, garbage out." The Clinton campaign believed the polls and strategized accordingly, and ultimately, lost.

One of the biggest surprises of the past year was that so many people still maintain traditional mindsets when approaching BI-related big data challenges. We’ve now known for nearly a decade that legacy technologies like relational databases cannot handle the volume of big data or the ever-increasing number of users these projects serve. On the data platform side, evidence such as the rapid growth of vendor revenues (at least by the publicly traded companies) suggests that businesses understand the need for modern platform technologies; yet, those lessons learned have not completely crossed over to the BI and analytics side. It seemed this year that the modern BI vendors are doing particularly well when cleaning up after failed attempts with legacy BI on big data as opposed to joining in on businesses’ initial big data journeys.

We’ve also seen one additional surprise, and this one centers around Hadoop. Leading providers and industry conferences started to downplay or even abandon references to Hadoop this past year, and we have seen a significant push for organizations to stop focusing on specific technologies. This shift has been due, at least in part, to wanting to avoid overhyping big data tools as some sort of panacea. It’s an odd and surprising shift, considering Hadoop is still a growing, core technology for a lot of big data deployments. Like with any big data tool, the key to success is squarely set in the company’s ability to correctly implement the technology.

In 2017, enterprises finally started to get a handle on how to use data and analytics solutions to leverage the data that is meaningful to their core business to drive outcomes and revenue. A lot of "swamp consolidation" occurred.

In 2017, anxiety on the part of business leaders over their digital journey reached a fevered pitch. Dell research of 4,000 worldwide business leaders showed that 45% feared their business would become obsolete in three to five years; 48% were not sure what their industry would even look like in three years; and 78% (practically all of them) felt threatened by digital startups.

I’m surprised at how fast people are moving into a post-Hadoop world in which they are looking for ways to build sophisticated (enterprise-ready) big data systems that deliver value in real-world situations.

Among the biggest reoccurring themes of 2017 was the unrelenting, exponential increase in data volumes bombarding enterprise customers. Along with that was the high proportion of these organizations seeking solutions to help perform rigorous discovery on their data to truly understand it. Business and IT executives around the globe endeavored to leverage their organizational data, as they perceived its tremendous value, but they were also frustrated with the amount of time and budget required to do so. Additionally, many were equally concerned that the pending GDPR (General Data Protection Regulation) requirements would exacerbate current challenges, further motivating their search for discovery and governance solutions.

The biggest surprise in 2017 was the confusion in the stream processing market with regard to which stream processing framework to use. Apache Flink, Spark Streaming, Kafka Streams, and other alternatives emerged, all of which on the surface offer similar capabilities. Businesses that use these frameworks are scratching their heads as to which one to use, wondering if a clear leader emerges. The net result is an unwanted side-effect: solution sprawl and a lack of oversight and control over ingested data.

2017 was a year of large momentum for cloud analytics. We saw even the highest regulated industries going all-in on cloud analytics in order to take advantage of technical and cost efficiencies far beyond on-premise solutions.

The pace and place of automated and AI-assisted technology in decision-making. Machine learning and AI technology have made significant advancements in just the past six months. This automation will have a profound impact on the use of analytics; uncovering insights in large data volumes and provide easy and contextual insights for users.

Analytics pairings; a tech-recipe for digital acceleration. Not surprising that research shows growth leaders use analytics, but analytics in 2017 was at an intersection alley of a growth digital innovation spectrum as IoT, sensor, streaming data, machine learning, blockchain, and business data networks combine to deliver accelerated business growth opportunities.